The present disclosure relates to analysis and display of information in small format visual analytics, and in particular, to automatically labeling space constrained small format visual analytics.
Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
To display as much relevant information in often space constrained display devices, various types of small format visual analytics have been developed. As with other sizes of visual analytics, small format visual analytics may include any type of visualization of the underlying data or analysis. For example, the visualizations may include charts, graphs, metric values, etc., or some combination thereof. To increase the effectiveness of the visualization, some level of context can be provided for the visualization to be immediately meaningful. In general, context can be added to a visualization by labels included in the visual analytic. The labels can include the specification of a titles, units, scale, time, and the like. In relatively large format visual analytics, where space is not an issue, automatically adding labels to the various aspects of the visualization is a simple matter of parsing and including descriptive metadata from the analytic data and associating it with the visualization.
However, in smaller format visual analytics, all the information included in the metadata cannot always be included in a size that is intelligible while also maintaining the effectiveness of the data visualization. An individual small format visual analytic is typically constrained by particular size frame or boundary. Accordingly, if the size of the alphanumeric text used for the labels is too large, then it would be very difficult to include more than only a portion of the metadata before running out of room or occluding information represented in the visualization. On the other hand, if the size of the alphanumeric text used for the labels is made to be small enough to fit labels containing all of the metadata, then it may be too small to be legible or appear to be cluttered, thus distracting from the clarity of the associated visualization.
To avoid labels from becoming too large or growing too small, many information and analytic systems rely on users to manually determine and provide labels for any and all of the visual analytics the system produces. However, manually labeling visual analytics presents significant problems that prevent such systems from scaling to larger scale automatic generation of small format visual analytics.
As the ability of information and analysis systems to generate useful small format visual analytics automatically from the underlying analytic data and metadata increases, the potential for including too much or too little information in the labels also increases. The number of visual analytics that may be generated can quickly become unwieldy. Manually labeling each visual analytic in a clear and concise manner can become overly arduous, if not impossible. Additionally, leaving the labeling to up to individual users can cause inconsistencies in the manner in which visual analytics are labeled, thus reducing their overall effectiveness in communicating information to a larger audience. Since intelligent labeling of the visual analytics is often just as important as the visualizations themselves, an automated systems and method for labeling small format visual analytics present significant advantages.
Thus, there is a need for improved system and method for generating labeling. The present invention solves these and other problems by providing a system and method for rule based space constrained small format visual analytic labeling.